RESUMO
Online social media has been evolved as a universal platform for sharing information. Termination being shared on these platforms can be dubious or filthy. Propaganda is one of the systematic methods by which behavior of user can be manipulated. In this work, various machine learning methods are used for detecting such types of information on online social media. Data is collected d from Twitter using its API with the help of various ambiguous hashtags. The results showed that proposed Long Short Term Memory (LSTM) based propaganda identification showed better results than other machine learning techniques. An accuracy of 77.15% is achieved using the proposed approach. In the future BERT model can be used for achieving better Accuracy.
RESUMO
People share their views and daily life experiences on social networks and form a network structure. The information shared on social networks can be unreliable, and detecting such kinds of information may reduce mass panic. Propaganda is a kind of biased or unreliable information that can mislead or intend to promote a political cause. The disseminators involved in spreading such information create a sophisticated network structure. Detecting such communities can lead to a safe and reliable network for the users. In this paper, a Boundary-based Community Detection Approach (BCDA) has been proposed to identify the core nodes in a propagandistic community that detects propagandistic communities from social networks with the help of interior and boundary nodes. The approach consists of two phases, one is to detect the community, and the other is to detect the core member. The approach mines nodes from the boundary as well as from the interior of the community structure. The leader Ranker algorithm is used for mining candidate nodes within the boundary, and the Constraint coefficient is used for mining nodes within the boundary. A novel dataset is generated from Twitter. About six propagandistic communities are detected. The core members of the propagandistic community are a combination of a few nodes. The experiments are conducted on a newly collected Twitter dataset consisting of 16 attributes. From the experimental results, it is clear that the proposed model outperformed other related approaches, including Greedy Approach, Improved Community-based 316 Robust Influence Maximization (ICRIM), Community Based Influence Maximization Approach (CBIMA), etc. It was also observed from the experiments that most of the propagandistic information is being shared during trending events around the globe, for example, at times of the COVID-19 pandemic.
RESUMO
The latest trend of sharing information has evolved many concerns for the current researchers, which are working on computational social sciences. Online social network platforms have become a tool for sharing propagandistic information. This is being used as a lethal weapon in modern days to destabilize democracies and other political or religious events. The COVID-19 affected almost every corner of the world. Various propagandistic tweets were shared on Twitter during the peak time of COVID-19. In this paper, improved artificial neural network algorithm is proposed to classify tweets into propagandistic and nonpropagandistic class. The data are extracted using multiple ambiguous hashtags and are manually annotated into binary class. Hybrid feature engineering is being performed by combining "Term Frequency (TF)/Inverse Document Frequency (IDF)," "Bag of Words," and Tweet Length. The proposed algorithm is compared with logistic regression, support vector machine, and multinomial Naive Bayes. Results showed that improved artificial neural network algorithm outperforms other machine learning algorithms by having 77.15% accuracy, 77% of recall, and 79% precision. In future, deep learning approaches like LSTM may be used for this classification task.